reduced productivity and lack of concentration. Noise during thenight disrupts sleep patterns. Noise pollution results in elevated stresslevels and can also increase the risk of cardiovascular and circulatoryproblems (1).

The pollution of auditory surroundings not only diminishes thequality of life but also devalues the environment as a whole.

VALUE OF THE ENVIRONMENT

One of the problems associated with assessing the impact of noisepollution is the relative nature of sound measurement. Apart fromthe fact that sound levels are measured on a logarithmic scale, noiseintrusion can often be highly relative to the listener’s surroundings.The impact of noise events is commonly compared with the back-ground or ambient noise level and so is not solely dependent on thedecibel level of the noise. As a result, it is difficult to quantify thepersonal or environmental impact of particular noise levels. A numberof approaches have been adopted over the years that aim to put avalue on noise reduction, either by gauging the annoyance to thepublic or by estimating the economic value of noise abatement. Arecent study by a European Union (EU) working group of noiseexperts suggests a method based on dose–effect relationships forevaluating the number of people annoyed by noise from a givensource (2). The dose–effect relationship is the relationship betweenthe dose of harmful factors (i.e., noise) and the severity of theireffect on exposed subjects.

This position paper (essentially an advisory paper for the Euro-pean Council) uses the percentage of people annoyed (%A) andpercentage of people highly annoyed (%HA) as metrics for noise-related annoyance in a community. The noise indicator used isLden, as selected for noise annoyance in the EU noise directive (3).Lden is a noise-level reading that takes account of day, evening, andnighttime noise. Weightings are applied to the different periodsto account for the fact that noise can be more annoying at differenttimes of the day. The evening period (7:00 to 11:00 p.m.) carries aweighting of +5 dB, and nighttime noise (11:00 p.m. to 7:00 a.m.)carries a penalty of +10 dB. The Lden represents the yearly averagenoise level.

The use of dose–effect relationships produces results that aremore comprehensible for the general public; not everyone is familiarwith the Lden measurement but a useful measure of how annoying aparticular noise source is can be easily understood. The workinggroup proposed expressions for estimating the %A and %HA for agiven source of transportation noise. The position paper also outlinespossible mitigation strategies that may arise from the calculation of

Assessing Environmental Impact of Transport Noise with Wireless Sensor Networks

Paul McDonald, Dermot Geraghty, Ivor Humphreys, and Stephen Farrell

133

Noise pollution from transportation systems devalues the environmentand carries with it substantial social and economic costs. New legislationaims to reduce the impact of transport noise. This study outlines howwireless sensor networks can be used to assess accurately the socio-economic benefits of noise mitigation policies. Various economic methodsof estimating the disamenity of noise are explored. Current practices forpredicting the benefits of noise reduction strategies are examined, andthe need for a long-term monitoring solution within the current systemis observed. Sensor network with delay tolerance (SeNDT) units are pre-sented as a wireless sensor networking device capable of monitoringenvironmental noise levels. Preliminary results from the SeNDT pilotstudy deployments are presented. These readings are used to estimateboth the personally perceived and monetary gains resulting from theimplementation of a new traffic policy that bans heavy goods vehicles inthe city center of Dublin, Ireland. Initial calculations indicate that theban will reduce the number of people annoyed by road traffic noise byapproximately 8% and constitute a monetary saving of 677.50 ($105)per household in the area per year.

As the scale of transport systems has increased in recent decades, sotoo has the noise generated by these systems. Whether caused byroad, rail, or air travel, the amount of noise pollution and also thenumber of people affected by it have grown dramatically. In recentyears relevant authorities have begun to address this issue. Govern-ments have drawn up legislation to combat noise pollution, effortshave been made to minimize the number of people exposed to noisefrom transport systems, and several studies have been conducted totry to help better understand the effects of noise exposure.

It is difficult to quantify noise pollution definitively. Certainlynoise levels that damage one’s hearing are harmful, but what aboutnoise that does not physically harm people but simply annoys them?Again it is hard to define exactly what constitutes annoyance orwhat its consequences are. Whether it can be directly quantifiedor not, annoyance due to noise pollution has been shown to haveseveral detrimental effects on people’s health. Traffic noise has beenshown to affect children’s learning. Noise in the workplace leads to

P. McDonald, D. Geraghty, and I. Humphreys, Department of Mechanical andManufacturing Engineering, School of Engineering, and S. Farrell, Department of Computer Science, Trinity College, Dublin 2, Ireland. Corresponding author: P. McDonald, pmcdonal@tcd.ie.

Transportation Research Record: Journal of the Transportation Research Board,No. 2058, Transportation Research Board of the National Academies, Washington,D.C., 2008, pp. 133–139.DOI: 10.3141/2058-16

these data. The following abatement and prevention measures aresuggested:

• Elimination of unacceptable noise levels by imposing a legallimit in terms of Lden, possibly linked to the type of source;

• Preservation and extension of quiet areas both residential andnatural; and

• Improvement of acoustic environment in areas where Lden isarbitrarily deemed to be too high (2).

Current EU policy aims to reduce the effects of transport noiseon the general population and will no doubt continue to be soaimed for the foreseeable future. The success of these policies canonly be quantified by the employment of useful methodologiesand metrics such as the dose–effect relationship, provided that theunderlying data accurately represent the situation. In order to fullyevaluate the impact of noise pollution, or indeed noise prevention,accurate measurement of noise emissions from transport systemsis necessary.

But what about the more indirect impacts of noise pollution?Quantifying annoyance is indeed useful and provides a means ofdescribing noise in a way that is easy to understand. However, thereis another metric that everyone can understand—money. What is thecost of environmental noise? In 1992 the French National PlanningOffice used dose–effect relationships to calculate a value of 6137($187) per person annoyed per year (4). In addition to annoyanceand other personal factors, noise pollution has several detrimentaleffects in an economic sense. During the 1980s and early 1990snumerous studies were carried out aimed at estimating the costs oftraffic noise. They examined areas such as losses in property value,productivity losses, costs voluntarily incurred by the public, andgovernment expenditure on abatement strategies. Soguel (5) esti-mated that the people of Neuchâtel in Switzerland were willing topay approximately 6710 ($970) per annum to reduce their expo-sure to traffic noise by half (1). A more recent study by Bjorner inCopenhagen, Denmark, estimated that at 65 dB the inhabitants werewilling to pay 66 ($8) per decibel reduction per year (6).

These studies were compiled with the contingent valuation method,an economic and statistical tool used to estimate a monetary value forintangible things like “peace and quiet” by surveying the attitudesof the general public. This stated-preference approach involvesquestioning a representative sample of the population about howmuch they would be willing to pay for a reduction in noise exposure.Their willingness to pay (WTP) can refer to preventive measuressuch as home insulation or increased rent and house prices to live ina quieter area. Some researchers believe that a loss in property valuecan be seen as a marginal WTP by home owners for small changesin exposure (7 ).

One such survey was carried out by Feitelson et al. in 1996 (8) thatdealt with the prices of houses affected by airport noise and used thecontingent valuation method. Feitelson used a noise depreciationindex (NDI) to estimate the loss in property value due to noise fromair transport. The NDI is the percentage reduction in house price perdecibel of noise exposure (usually assuming a given base level).They proposed an NDI of 1.5% loss of property value for houses inthe area (8). This value was calculated by asking local residentshow much they would be willing to pay for a house that was essen-tially the same as their own but free from airport-related noisepollution. They were also polled on how much they would pay forsuch a house subject to varying levels of noise (Lden). These valuesmay have been slightly excessive, but they do give an indication as

134 Transportation Research Record 2058

to the large amount of money people are willing to pay for a quieterenvironment.

Although they apparently provide a good insight into the atti-tudes of the public toward noise pollution, the results of these stated-preference surveys can be distorted by other factors. Responses canbe biased by several influences; for example, a respondent might notfully understand the hypothetical scenario he or she has been askedto imagine. In the case of noise measurements there is even morechance of this lack of understanding; it must be made clear that ahalving of noise exposure does not mean a 50% reduction in thedecibel level but rather a drop of about 10 dB. The surveys must becarefully constructed to avoid any ambiguity. A respondent mighttake into account the fact that reduced traffic flow would also resultin fewer carbon emissions and less congestion. This assumption couldlead to a response that is not wholly related to noise reduction.

Another technique of measuring the cost of noise that takes accountof market trends rather than public attitudes is the hedonic houseprice method, which is a revealed-preference approach, meaningthat results are based on hard data from property markets and thepossibility that personal biases might affect the outcome is eliminated.The aim is to analyze house prices in a given area and attempt toquantify the loss in property value attributable solely to transportationnoise, assuming all other things are equal.

The primary metric used in the hedonic pricing technique is theNDI. A number of studies carried out in Europe and the United Statesregarding transport noise, particularly traffic-related noise, assignedappreciable devaluation of property to noise pollution. In 1978Nelson estimated an NDI of 0.88% for Washington, D.C. (9, 1).Proposed values ranged as high as 1.3% of house prices per decibelin Basel, Switzerland (1). A survey by the Center for Social andEconomic Research into the Environment (CSERGE) and Environ-mental Friendly Technology (EFTEC) in 1994 suggested an averageNDI of 0.67% (10, 1); however, it should be noted that this estimateassumes that the characteristic results of different studies are trans-ferable between countries and cities. Although the data used in thissurvey are more than 20 years old and more up-to-date studies maybe more accurate and representative, this finding still indicates thatin general, property value losses amounting to billions of dollars canbe attributed to noise pollution.

Governments and local authorities in both Europe and the UnitedStates are now charged with developing noise action plans. An actionplan is concerned with the implementation and effectiveness ofnoise-reducing measures. Policy makers must be able to determineboth the feasibility and environmental benefits of these plans. To doso, they will require a detailed cost–benefit analysis. Decision makerswill then be able to directly assess, in monetary terms, the rewardsof noise mitigation strategies. A full cost–benefit analysis requiresthat a monetary value be assigned to a reduction in noise levels.A position paper published by the EU Working Group on Healthand Socio-Economic Aspects recommended a value of 625 ($34) perhousehold per decibel per year (11). The value was based on a studyconducted by Navrud in 2002 (12) and is a suggested value to beused by EU member states in the absence of more localized figures.According to that study, one can now put an approximate price onnoise: a decibel is worth 625. But to be able to actually count the costof noise pollution, the exact noise level due to transportation sourcesmust be known. This area is where a reliable monitoring system togauge the actual noise impact of transportation schemes is needed.Estimates cannot be made of the economic or personally perceivedgains of noise reduction plans without being able to accuratelymeasure their effect on environmental noise levels.

NOISE MONITORING

In 2002 the European Commission published the European noisedirective (3). Its goal is to harmonize noise assessment throughoutits member states. It also aims to provide a framework for local author-ities to maintain a high level of health and environmental protection.The document lays out several guidelines for assessing noise pollutionand for relating those data to the public. It specifies Lden as the noiseindicator for gauging annoyance and Lnight for evaluating disruptionto sleep patterns. Under the terms of the directive, member states areobliged to produce strategic noise maps for all major cities andtransportation networks, specifically

agglomerations with more than 250,000 inhabitants and for all majorroads which have more than six million vehicle passages a year, majorrailways which have more than 60,000 train passages per year andmajor airports within their territories. (3)

These maps will then be used in the formation of noise action plans.Noise maps may take several forms, such as tabulated data or

data in electronic form, but the most common format is a graphicalrepresentation of the noise levels in an area. Color-coded contourplots show the areas subject to the highest noise levels and linkareas of equal noise exposure. Figure 1 shows a sample noise mapof the Trinity College Dublin campus (13); dark areas located inthe center and at the peripheries of the map represent quiet zones(<50 dB) and areas on the major roadways represent the noisiestzones (70 to 75 dB).

Current noise-mapping techniques employ predictive softwarethat estimates the noise level in an area from a particular source given

McDonald, Geraghty, Humphreys, and Farrell 135

several governing factors, such as speed of traffic flow, number oflight and heavy vehicles, road surface and gradient, and buildingtopology. The common indicator is Lden. The software calculates thepredicted sound level accounting for all these factors. Accordingto the EU directive, the first set of maps for the areas mentionedearlier were deliverable by June 2007 and must be re-evaluated every5 years thereafter. These maps will be used in the development ofnoise action plans and will provide a means of disseminating data tothe public.

But what are the shortfalls of these maps? The plots are estimatesbased on the assumption that the contributory factors remain moreor less constant over the course of a year. It is generally seen asinfeasible to base noise maps solely on noise-monitoring data becausea prohibitively large number of sampling points would be needed.However, in Madrid, Spain, the City Council has done just that (14).Data are logged by using a number of mobile monitoring units, andthis information is fed into the mapping software. As far as the authorsare aware, Madrid is the only city in which this approach has beenadopted. So maps are usually just predictions. Accordingly, it isnecessary to calibrate and subsequently validate these maps with realdata. And since Lden is a yearly average it is reasonable to concludethat any real monitoring data should be taken over a long periodrather than take a discrete sample.

One of the fundamental concepts referred to throughout thedirective is that data must be made readily available to the publicin a manner that is clear and accessible. Noise maps, however,could be misleading since they only display noise from a singlesource. This restriction is certainly necessary in order to attributethe correct level of noise to a particular transport mode, but it does

Lden [dB(A)]

37 – 5051 – 5556 – 6061 – 6566 – 7070 – 7576 – 80

FIGURE 1 Noise map of Trinity College Dublin, Ireland.

not present a clear overall picture. King and Rice (15) addressedthe issue, saying:

It is clear that measurements must be taken to supplement the predictednoise maps and provide public assurance. These measurements willthen account for various aspects outside the control of the predictionmodel. (15)

Real data can then be used to increase public confidence in mappeddata and to provide a more complete representation of noise levelsin an area. Measured data could possibly be linked to amalgamatednoise maps, which would give a more realistic view of transport noise.

Although relating information to the general public is an importanttheme throughout the directive, the main reason for noise mappingis to assist relevant authorities in the formulation of noise actionplans. These noise abatement policies will include traffic planning,technical measures at noise sources, and measures for the reductionof sound propagation. They must also provide financial information,such as budgets, cost-effectiveness assessments, and cost–benefitanalyses (3). This information cannot be calculated by using predic-tions and estimates alone. In order to accurately assess the effect of anynoise reduction strategies, it will be necessary to take real measure-ments to indicate the real changes in noise levels. It is not enough toapply the noise reduction properties of, say, a barrier to a model. Theeffect must be measured at the location where the noise is perceived,that is, at the home or property affected (12).

Although prediction software may give an indication of noiselevels in an area, in situ noise monitoring is needed to provide a reli-able before-and-after picture of transport-related noise. Accurate harddata will allow planning authorities to directly quantify the effective-ness of noise mitigation policies and, by using the methods outlinedearlier, measure in monetary terms the benefits of their actions.

WIRELESS SENSOR NETWORKS

Wireless sensor network (WSN) research is an area that has pro-gressed rapidly in the past decade. The idea of “smart environments”has emerged as the vision and goal of many advocates of the tech-nology. By developing and deploying sensors to passively monitorone’s surroundings, one can gather and transfer data with an easethat was previously impossible.

The general concept behind WSNs is that a number of wireless(radio-equipped) devices are deployed in an area of interest tomonitor some pertinent parameter. The data are collected automat-ically and subsequently transferred through the network to a datarepository for postprocessing. This overview is a very simplified one,but the general principle can be applied to most WSN scenarios.There are a vast range of possible applications for WSNs, for instance,zebra tracking in Kenya, countersniper systems, and volcano mon-itoring (16–18). One field in which the technology is particularlysuitable is environmental monitoring. The wireless nature of thedevices means that data can be acquired and later accessed in loca-tions where no intelligent transportation infrastructure or facilitiesare available. Many environmental monitoring applications requirethe sensing units to be capable of logging for extended periods oftime or surviving in relatively harsh conditions.

Sensory Networks with Delay Tolerance

Trinity College Dublin has developed a data acquisition systemcapable of meeting the demands of long-term environmental noise-

136 Transportation Research Record 2058

level monitoring, sensory networks with delay tolerance (SeNDT).Designed as a platform for delay tolerant networking (DTN) appli-cations, the units are both physically and functionally robust. DTNprotocols were originally developed for deep space communicationsin which an end-to-end path between communicating entities wouldalmost never exist. The terrestrial incarnations of the technologyassume that sensor nodes will be operating in locations or conditionswhere there may not always be a link to a network. Data are transferredonly when the chance arises, either through opportunistic or scheduledcontacts. SeNDT units were designed with these possible scenariosin mind and so possess several qualities that make them ideallysuited to long-term environmental monitoring.

Compared with more commonplace sensor platforms (e.g., motes),the units are relatively high powered and high performance. Manyenvisaged sensor network scenarios tend toward dense deploymentsof small sensing units with limited power and range (∼10 m). Oneof the main problems associated with this approach is the cost,approximately $130 per unit (19). Although these small sensor plat-forms will no doubt in time become cheap enough to allow large-scaleinstallations, at the moment they are simply too expensive to permitthe deployment of enough nodes to achieve the coverage needed fora situation like urban noise monitoring. The extra power consump-tion of SeNDT units is balanced by increased communications rangeand sensing capability.

SeNDT nodes are built around an Intel XScale 255 processor,supplied as part of the Triton XXS processor board. The processor has64 MB of SDRAM, and the Triton board has 32 MB of onboard flashmemory. The XScale includes digital signal processing (DSP) exten-sions specifically aimed at the real-time processing of audio signals.The Triton board is attached to the main SeNDT input–output (I-O)board, which is equipped with numerous communication and sensoryinterfaces. The primary communication system is the 802.11b wire-less link. The nodes also have USB and RS232 ports (20). In additionto the Triton’s onboard flash memory, the units include a PersonalComputer Memory Card International Association compact flashcard with up to 4 GB of storage capacity. Audio data are acquiredthrough the high-performance analog front end. The nodes have fourinput channels, simultaneously sampled by a 16-bit analog-to-digitalconverter. Each of the channels is equipped with a low-cost electretmicrophone that converts changes in acoustic pressure to electricalsignals. The signals are sampled at a rate of 49 kHz, more thansufficient for the capture of high-quality audio data.

Once the data are sampled, they are passed through a digital A-weighting filter. This filter is one that assigns weightings to certainfrequency components of an audio signal, to account for the fact thatthe human ear is more sensitive to some frequencies than others. Thefiltered data are then converted to sound pressure levels and usedto calculate the desired noise level measurements. Figures 2 and 3show a SeNDT noise-monitoring unit and a populated I-O board,respectively.

The initial pilot study of the technology was conducted with thehelp of Dublin City Council and the Irish National Roads Authority.SeNDT units were deployed at a number of urban and motorwaylocations around Dublin to monitor traffic noise levels. The siteschosen were subject to high volumes of both commercial and com-muter traffic and qualified as mandatory points for noise mapping.In the case of the motorway locations the noise can be directly attrib-uted to traffic noise alone, since there are no other sound sources inthe vicinity. At the urban sites there may be noise contributions fromother transport modes. As suggested earlier, it is more useful to havethis overall picture that is fully representative of the actual noise

levels at the location rather than just one constituent part thereof.When information is provided to the public, it is necessary to pre-sent data that give a real indication of the noise levels. Although aparticular transport mode may not in itself constitute a noise problem,it may well increase noise levels in an area to an unacceptable level.It is certainly necessary then to understand the contribution ofindividual transport modes to overall noise levels, but these must beamalgamated when the data are disseminated to the public at large.It is for this purpose that a sufficiently widespread and cost-effectivenoise-monitoring network is needed.

Example

On the February 19, 2007, Dublin City Council introduced a citycenter ban on heavy goods vehicles (HGVs) with five or more axles.The ban is in place from 7:00 a.m. to 7:00 p.m. and was brought intoforce to try to reduce the amount of heavy commercial traffic aroundthe inner city and on the Dublin quays. The aim was to make the city

McDonald, Geraghty, Humphreys, and Farrell 137

center less congested and safer for pedestrians and cyclists and tolower air pollution and, of course, reduce noise levels.

By installing a long-term noise-monitoring system in strategiclocations, local authorities such as Dublin City Council can calculatethe actual savings and monetary gains brought about by their policiesand decisions.

Figures 4 and 5 show representative samples of A-weighted noiselevels on the Dublin quays before and after the ban, respectively.The quays are heavily populated, with a large number of apartmentbuildings facing the river. This is an area of ongoing developmentwith several more residential properties planned in the coming years.

The Leq is the average sound level over a given period. The L10 isthe sound level exceeded 10% of the time; this measurement concernspeak noise events that would otherwise be averaged out. The L95 read-ing is the noise level exceeded 95% of the time, which is essentiallythe background noise level. Calculations of the Lden values weremade by using the equation below, as defined by the EU (3):

In the foregoing equation the day, evening, and night terms refer tothe long-term A-weighted Leq for the specified time period. A prelim-inary analysis based on all available readings (approximately 1 monthbefore the ban and 3 months after) indicates a drop of 3.1 dB(A) innoise levels. It should be pointed out that a proper evaluation ofthese before-and-after conditions should make use of a much largerdata set, for example, 1 year, to accurately calculate Lden. However,by using the available data some initial observations can be made.

The EU working group suggests the following polynomials forcalculation of the percentage of people annoyed (%A) and highly(%HA) annoyed because of road traffic noise (2):

With these expressions and the calculated value of Lden, one canestimate the benefits of the HGV ban. There is a reduction of 8.89%in the number of people annoyed by road traffic noise and a reductionof 8.33% in the number of people highly annoyed. In addition to thepersonally perceived advantages of the policy, the financial impact canbe assessed. With the value suggested by the EU working group (11),initial calculations indicate a monetary benefit of 677.50 ($105)per household per annum for the HGV ban. Given the large numbersof residential properties in the area, this value represents significantsavings.

DISCUSSION AND FUTURE WORK

Real data are necessary to accurately assess the effectiveness ofchanges brought about by noise reduction policies. Predictions cannotaccount for all factors governing noise pollution. The results reportedhere indicate how a WSN can be used to gather long-term environ-mental noise data. This system can be left unattended for months ata time and data subsequently collected in a simple manner. WSNscan provide a reliable and feasible method of long-term environ-mental noise monitoring. The results presented here are an example

% . .HA den den= −( ) − −(− −9 868 10 42 1 436 10 424 3 2� �L L ))+ −( )

2

0 5118 42. Lden

% . .A den den= −( ) + −( )− −1 795 10 37 2 11 10 374 3 2 2� �L L

++ −( )0 5353 37. Lden

Lden

day evening ni

= + +÷

1012 10 4 10 8 1010 5 10

log� � � gght+⎡

⎣⎢

⎤⎦⎥

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24

FIGURE 2 SeNDT noise-monitoring unit.

FIGURE 3 Populated SeNDT I-O board.

of how real data can be used to directly assess the socioeconomicgains of noise reduction methods.

The value of 3.1 dB(A) was slightly lower than expected (3 dB isabout the limit of perceptible changes in sound levels). This resultis most likely due to the fact that the elimination of HGVs from thecity center has reduced congestion and traffic is faster-moving. Also,after 7:00 p.m. many HGVs use city center routes that are by thenfree-flowing. A large proportion of vehicle noise is due to tire–roadinteraction, and smaller volumes of fast-moving traffic can often belouder than larger yet slower volumes. This fact may account for theseemingly small reduction in noise levels, although a longer data setwill reveal a clearer picture when available.

It should also be noted that the noise levels shown in these resultsare overall noise levels. Although the monitoring point was not inthe vicinity of any other sources of transport noise, there may be someinfluences other than road traffic alone. It is planned to increase thesignal processing capabilities of SeNDT units to allow them to dis-tinguish between noise sources. With sufficient spectral analysis it

138 Transportation Research Record 2058

will be possible to identify noise sources from a particular transportmode such as rail or aircraft. The data sets can then be amalgamatedto form an overall picture of transport noise or used individually tovalidate noise maps.

Further ongoing improvements to the SeNDT nodes include fullyautomating data collection by using mobile nodes. A mobile node inthe form of a municipal vehicle, for example, a garbage truck, will beinstrumented as a data mule. A data mule is a mobile unit that collectsdata automatically from stationary monitoring points as it travels alonga certain route. By using the delay-tolerant capabilities of SeNDT, datacollection can be fully automated with guaranteed reliability.

CONCLUSION

In recent years there has been an increasing amount of time andresources dedicated to investigating the effects of environmental noise.By far the most prevalent source of noise pollution is transportation.

Mon Tue Wed Thu Fri

10

0

20

30

40

50

60

70

80

90

100

No

ise

Lev

el (

dB

A)

Leq L10 L95

FIGURE 4 Noise levels on Dublin quays, January 15–19, 2007.

Mon Tue Wed Thu Fri

10

0

20

30

40

50

60

70

80

90

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No

ise

Lev

el (

dB

A)

Leq L10 L95

FIGURE 5 Noise levels on Dublin quays March 12–16, 2007.

The systems and modes that are employed worldwide every daypollute the acoustic environment on a vast scale. Research has shownthat this noise pollution has detrimental effects on both a personaland an economic level. The health of the individual can be harmedand substantial money lost as a result of excessive transport noise.

Governments are now beginning to address these issues by enactinglegislation aimed at protecting the general public and the environmentfrom the effects of noise pollution. To be successful in this, thoseresponsible must be able to quantify the noise impact of transportationsystems and identify areas where change is needed. Various methodsexist for assigning monetary and personal value to noise reduction.But for these methods to be usefully employed the data regardingthe levels of noise pollution must be accurate and fully representativeof the scenario at hand. The use of a widespread monitoring systemwill allow policy makers to accurately assess the effectiveness oftheir noise mitigation strategies. For such a widely distributed systemto be practical, data collection must be simple and reliable. A usefulnoise-monitoring system must be capable of collecting data forextended periods of time with little or no human interaction. WSNsare the solution to this problem.

REFERENCES

1. Maddison, D., and D. Pearce. The True Costs of Road Transport.Earthscan Publications Ltd., London, 1996.

2. European Union Working Group Dose/Effect. Position Paper on DoseResponse Relationships Between Transport Noise and Annoyance. Officefor Official Publications of the European Communities, Luxembourg,Feb. 20, 2002 (also available on the European Commission website).

3. European Parliament and Council of the European Union. Directive2002/49/EC of the European Parliament and of the Council. OfficialJournal of European Communities, 2002.

4. Lambert, J. Using Monetary Values of Noise for Transport PolicyDecisions: Current Practices and Future Developments in France. Pre-sented at InterNoise 2000: 29th International Congress and Exhibitionon Noise Control Engineering, Nice, France, Aug. 2000.

5. Soguel, N. Costing the Traffic Barrier Effect: A Contingent ValuationSurvey. Institute de Recherches Economique et Régionales (IRER),Université de Neuchâtel, Switzerland, 1994.

McDonald, Geraghty, Humphreys, and Farrell 139

6. Bjorner, T. B. Comparing the Value of Quiet From Contingent Valua-tion and Hedonic Pricing Methods. AKF, Institute of Local GovernmentStudies, Copenhagen Denmark, 2004.

7. Nelson, J. P. Cost-Benefit Analysis and Transportation Noise. Presentedat National Academy of Engineering Workshop on Transportation NoiseControl Technology, Volpe Transportation Systems Center, Cambridge,Mass., Feb. 2007.

8. Feitelson, E. I., R. E. Hurd, and R. R. Mudge. The Impact of AirportNoise on Willingness to Pay for Residences. Transportation Research,Vol. 1D, No. 1, 1996, pp. 1–14.

9. Nelson, J. P. Highway Noise and Property Values. Journal of TransportEconomics and Policy, May 1982, pp. 117–138.

10. CSERGE/EFTEC. A Survey of Noise Cost Estimates. CSERGE, Uni-versity College London and University of East Anglia, United Kingdom,1994.

11. European Union Working Group on Health and Socio-Economic Aspects.Valuation of Noise. Dec. 2003.

12. Navrud, S. The State of the Art on Economic Valuation of Noise. FinalReport. European Commission, Directorate General Environment,2002.

13. King, E., and H. Rice. The Development of an Independent Noise Pre-diction Model. Presented at 19th International Congress on Acoustics,Madrid, Spain, Sept. 2007.

14. Manvell, D. Sadmam: Combining Measurements and Calculations toMap Noise in Madrid. Presented at InterNoise 2004: 33rd InternationalCongress and Exposition on Noise Control Engineering, Prague, CzechRepublic, Aug. 2004.

15. King, E., and H. Rice. Refining Noise Maps for Public Dissemination.Presented at Euronoise 2006, Tampere, Finland.

16. Zhang, P. Hardware Design Experiences in ZebraNet. Presented atSenSys ’04, Baltimore, Md., Nov. 2004.

17. Simon, G. Sensor Based Counter Sniper System. Presented at SenSys’04, Baltimore, Md., Nov. 2004.

18. Werner-Allen, G., K. Lorincz, M. Welsh, O. Marcillo, J. Johnson, M. Ruiz,and J. Lees. Deploying a Wireless Sensor Network on an Active Volcano.IEEE Internet Computing, March 2006, pp. 18–25.

19. Moteiv Homepage. www.moteiv.com. Accessed July 24, 2007.20. McDonald, P., D. Geraghty, I. Humphreys, S. Farrell, and V. Cahill.

Sensor Networks with Delay Tolerance (SeNDT). Presented at ICCCN ’07,Sixteenth International Conference on Computer Communications andNetworks, Turtle Bay Resort, Kahuku, Oahu, Hawaii, Aug. 2007.

The Transportation-Related Noise and Vibration Committee sponsored publicationof this paper.